Machine Learning on AWS

Our automation reduces time to market by 70%

Our 8-step outcome-oriented solution design process include

  • Press Release and FAQ Document – This is an intensive session focused on outcomes. We then build a vision document covering every aspect of the final solution.
  • Envisioning Session – Our AI experts work with your team to explore possible solutions and outcomes using AI/ML advancements.
  • Data Exploration and Validation– We begin reviewing, compiling, and validating the available data. 
  • Data Preparation – Our team begins cleaning data, normalizing, and encoding features to start the initial training.
  • Model Selection– We examine the best performing models and determine the best solution to meet your needs.
  • Training and Validation– Once we’ve narrowed the selection to three models, we train and run validations on each before proceeding to final model selection. This is an iterative process, sometimes taking multiple weeks to reach the desired outcome.
  • Production Deployment– After a final model is selected, our Data Operations team will finalize the production deployment.
  • Post-Production Training – We set-up your ML Pipeline to continuously evolve and improve by identifying the periodicity of the training and data availability required. Then we automate the training, validation, and deployment of the improved model.

Case Study: Serverless pipeline on AWS Sagemaker

Learn how Poshmark built a serverless ML pipeline in 4 weeks to predict the shipping score.

Case Study: Optimize EDA workload using Axomo on AWS

Learn how Synaptics optimized their EDA workload using Axomo on AWS.

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